Detection device, determination method, and non-transitory computer-readable medium

ABSTRACT

A detection device ( 10 ) according to the present disclosure includes an acquisition unit ( 11 ) that acquires point cloud data indicating a distance from a measurement device to an object and luminance information obtained from reflected light of a beam emitted when the point cloud data is measured, an edge detection unit ( 12 ) that performs edge detection based on the luminance information, and a crack determination unit ( 13 ) that determines whether an area indicates a crack by using a shape of the area in which, of a plurality of points indicated by the point cloud data, points having luminance within a predetermined range of difference from luminance of a point detected as an edge are distributed.

TECHNICAL FIELD

This disclosure relates to a detection device, a determination method,and a program.

BACKGROUND ART

Techniques for detecting cracks in concrete by performing imageprocessing on images captured by a camera have been studied. Forexample, Patent Literature 1 discloses a configuration of a surfacedefect evaluation device that calculates feature values of defects fromimage data and identifies a set of areas of the feature values of thedefects on the image data. The defects on the image data correspond tocracks in concrete.

CITATION LIST Patent Literature

-   Patent Literature 1: Japanese Unexamined Patent Application    Publication No. 2016-217940

SUMMARY OF INVENTION Technical Problem

The quality of images captured by a camera is affected by the brightnessof the environment surrounding the camera. Therefore, when the surfacedefect evaluation device disclosed in Patent Literature 1 is used, it isnecessary to keep the brightness at a certain level during imagecapturing to acquire image data of images for effective imageprocessing. However, railroad tunnels, tunnels on roads, and the like,are often not equipped with sufficient brightness lighting over theentire area of the tunnel. Therefore, in order to inspect concrete intunnels, it is necessary to use large-scale lighting equipment.

A purpose of the present disclosure is to provide a detection device, adetermination method, and a program that are capable of detecting acrack even when brightness is not sufficient to acquire image data of animage for effective image processing.

Solution to Problem

A detection device according to a first aspect includes an acquisitionunit that acquires point cloud data indicating a distance from ameasurement device to an object and luminance information obtained fromreflected light of a beam emitted when the point cloud data is measured,an edge detection unit that performs edge detection based on theluminance information, and a crack determination unit that determineswhether an area indicates a crack by using a shape of the area in which,of a plurality of points indicated by the point cloud data, pointshaving luminance within a predetermined range of difference fromluminance of a point detected as an edge are distributed.

A determination method according to a second aspect includes acquiringpoint cloud data indicating a distance from a measurement device to anobject and luminance information obtained from reflected light of a beamemitted when the point cloud data is measured, performing edge detectionbased on the luminance information, and determining whether an areaindicates a crack by using a shape of the area in which, of a pluralityof points indicated by the point cloud data, points having luminancewithin a predetermined range of difference from luminance of a pointdetected as an edge are distributed.

A program according to a third aspect causes a computer to executeacquiring point cloud data indicating a distance from a measurementdevice to an object and luminance information obtained from reflectedlight of a beam emitted when the point cloud data is measured,performing edge detection based on the luminance information, anddetermining whether an area indicates a crack by using a shape of thearea in which, of a plurality of points indicated by the point clouddata, points having luminance within a predetermined range of differencefrom luminance of a point detected as an edge are distributed.

Advantageous Effects of Invention

With the present disclosure, it is possible to provide a detectiondevice, a determination method, and a program that are capable ofdetecting a crack even when brightness is not sufficient to acquireimage data of an image for effective image processing.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a configuration diagram of a detection device according to afirst example embodiment;

FIG. 2 is a diagram for explaining an analysis process for a set ofpoints according to a second example embodiment;

FIG. 3 is a diagram for explaining the analysis process for a set ofpoints according to the second example embodiment;

FIG. 4 is a flowchart showing a procedure of a crack determinationprocess of a detection device according to the second exampleembodiment;

FIG. 5 is a flowchart showing a procedure of a crack determinationprocess of the detection device according to the second exampleembodiment;

FIG. 6 is a diagram showing a shape of a crack according to a thirdexample embodiment;

FIG. 7 is a diagram showing the division of a space according to thethird example embodiment;

FIG. 8 is a diagram for explaining a point density according to a fourthexample embodiment;

FIG. 9 is a flowchart showing a procedure of a crack determinationprocess of a detection device according to the fourth exampleembodiment;

FIG. 10 is a configuration diagram showing a detection device accordingto a fifth example embodiment;

FIG. 11 is a diagram for explaining an incident angle and reflectedlight of a beam according to the fifth example embodiment;

FIG. 12 is a flowchart of a procedure of a process related to assignmentof a label according to the fifth example embodiment; and

FIG. 13 is a configuration diagram showing a detection device accordingto each example embodiment.

EXAMPLE EMBODIMENT First Example Embodiment

Hereinafter, example embodiments of the present disclosure are describedwith reference to the drawings. A configuration example of a detectiondevice according to a first example embodiment is described withreference to FIG. 1 . The detection device 10 may be a computer deviceoperated by a processor executing a program stored in a memory. Thedetection device 10 may be, for example, a server device. The processesto be executed in the detection device 10 may be distributed to andperformed on a plurality of computer devices.

The detection device 10 includes an acquisition unit 11, an edgedetection unit 12, and a crack determination unit 13. The acquisitionunit 11, the edge detection unit 12, and the crack determination unit 13may be software or modules to be processed by a processor executing aprogram stored in a memory. Alternatively, the acquisition unit 11, theedge detection unit 12, and the crack determination unit 13 may behardware, such as circuits or chips.

The acquisition unit 11 acquires point cloud data indicating thedistance from a measurement device to an object and luminanceinformation obtained from the reflected light of a beam emitted when thepoint cloud data is measured. The measurement device is a device thatmeasures point cloud data indicating the distance to an object. Themeasurement device may be, for example, a three-dimensional (3D) sensor.The 3D sensor may specifically be a 3D-LiDAR device. The 3D-LiDAR devicemeasures the distance to an object by using, for example, the Time ofFlight (ToF) technique to identify the shape of the object. The 3D-LiDARdevice may be referred to as a laser scanner. The object is an objectthat may be cracked and is, for example, a wall of a tunnel or abuilding.

In a case where the detection device 10 includes a measurement device,the acquisition unit 11 may be the measurement device. That is, theacquisition unit 11 operating as the measurement device directlyacquires point cloud data and luminance information as measurementresults. The case where the detection device 10 includes a measurementdevice includes a case where the detection device 10 and the measurementdevice integrally operate. Alternatively, the acquisition unit 11 may beconnected to a measurement device via a network. In this case, theacquisition unit 11 may receive point cloud data and luminanceinformation transmitted from the measurement device via the network. Theacquisition unit 11 may acquire the point cloud data and the luminanceinformation measured by the measurement device via a portable recordingmedium or the like.

The edge detection unit 12 performs edge detection based on theluminance information. For example, the edge detection unit 12 detects,for each point contained in the point cloud data, a point with thedifference from the luminance of an adjacent point exceeding aprescribed threshold as an edge. Specifically, the edge detection unit12 may detect, as an edge, a point having luminance that is lower thanthe luminance of an adjacent point by a prescribed threshold or more. Inaddition, the edge detection unit 12 may also detect, as an edge, apoint having substantially the same luminance as the point detected asan edge. Substantially the same luminance may be luminance within apredetermined range of difference from the luminance of the point of theedge detected because the difference from the luminance of the adjacentpoint exceeds the prescribed threshold. The value of the luminancewithin the predetermined range considered to be substantially the sameluminance is set to be sufficiently smaller than the value of thethreshold used to detect an edge based on the difference from theluminance of the adjacent point. In other words, the edge detection unit12 detects, as an edge, a set of points whose luminance lowers by theprescribed threshold or more. The set of points whose luminance lowersby the prescribed threshold or more is a crack candidate.

The crack determination unit 13 determines whether an area detected asthe edge indicates a crack by using the shape of the area in which, of aplurality of points indicated by the point cloud data, points having theluminance within the predetermined range of difference from theluminance of the point detected as the edge are distributed. In otherwords, the crack determination unit 13 uses the shape of the set ofpoints detected as the edge to determine whether the shape indicates acrack. For example, the crack determination unit 13 may define criteriafor at least one of the length and width of the edge considered to be acrack and determine that the set of points is a crack when the set ofpoints detected as the edge satisfies the prescribed criteria or thatthe set of points is not a crack when the set of points does not satisfythe prescribed criteria. The prescribed criteria may be, for example,that the length of the edge is greater than or equal to a predeterminedvalue, that the width of the edge is less than or equal to apredetermined width, or the like.

Alternatively, the crack determination unit 13 may determine whether theshape of the set of points detected as the edge indicates a crack byusing a learning model learned the shapes of cracks in advance bymachine learning or the like.

As described above, the detection device 10 detects an area that is acrack candidate by using luminance information associated with pointcloud data. The luminance information is information obtained fromreflected light when a beam is emitted to an object. Therefore, themeasurement device does not need to maintain the brightness of thesurrounding environment above a certain level in order to obtain theluminance information. As a result, the detection device 10 does notrequire the use of large-scale lighting equipment, even when inspectingconcrete or other objects in locations not equipped with sufficientbrightness lighting, such as in tunnels.

Second Example Embodiment

Next, an analysis process for a set of points detected as an edge to beperformed by an edge detection unit 12 is described with reference toFIG. 2 . The edge detection unit 12 detects, of a plurality of pointscontained in point cloud data, points to be an edge based on luminanceinformation. In addition, the edge detection unit 12 also detects, as anedge, points having the luminance of the points detected as the edge andluminance within a predetermined range.

FIG. 2 shows the distribution of the points detected as the edge by theedge detection unit 12 in an X-Y plane. Each point is 3D data and has avalue indicating its position on an X-axis, a Y-axis, and a Z-axis. TheX-axis, the Y-axis, and the Z-axis may be, for example, coordinate axesdefined by a 3D-LiDAR device. Each of the points shown in FIG. 2 hasluminance equivalent to the luminance of the other points. The luminanceof a point indicates the luminance of the reflected light of a beam whenthe beam is reflected at that point.

FIG. 2 shows the distribution of the points detected as the edge in theX-Y plane for ease of understanding, but the actual points detected asthe edge are also distributed in the Z axis direction perpendicular tothe X-Y plane.

The edge detection unit 12 performs a principle component analysis (PCA)on the distribution of the points detected as the edge as shown in FIG.2 . The principle component analysis is mainly performed to identify thevariance of points. Variance may be paraphrased as dispersion.

Specifically, the principle component analysis is performed to calculatethe magnitude or length of variance in the direction of the maximumvariance of the points as shown in FIG. 2 . The solid line shown in thedistribution of the points in FIG. 2 indicates the magnitude or lengthof the variance of the points. The magnitude or length of the variancemay be paraphrased as the eigenvalue. In FIG. 2 , D_1 indicates thelength of the variance in the direction of the maximum variance of thepoints in the distribution of the points detected as the edge. Here, D_1is defined as a first principle component. For example, the edgedetection unit 12 may calculate D_1 to be the first principle componentby calculating the center of gravity in the distribution of the pointsdetected as the edge and calculating the direction of the maximumvariance from the center of gravity.

Next, in the direction orthogonal to the first principle component, thelength of the variance in the direction of the maximum variance of thepoints is calculated. The solid line shown in the distribution of thepoints in FIG. 3 indicates the magnitude or length of the variance ofthe points. In FIG. 3 , D_2 indicates the length of the variance in thedirection of the maximum variance of the points in the directionorthogonal to D_1. Here, D_2 is defined as a second principle component.Next, in the direction orthogonal to the first and second principlecomponents, the length of the variance in the direction of the maximumvariance of the points is calculated. Although not shown in FIG. 2 , D_3is the length of the variance in the direction of the maximum varianceof the points in the Z-axis direction perpendicular to the X-Y plane inFIG. 2 . Here, D_3 is defined as third principle component.

By performing the principle component analysis on the distribution ofthe point cloud data, which is 3-dimensional data, the first principlecomponent to third principle component can be obtained.

The crack determination unit 13 determines whether the shape indicatedby the set of points detected as the edge corresponds to a crack byusing the first principle component to third principle componentcalculated by the edge detection unit 12. In the set of pointsindicating a crack, the first principle component is assumed to be thedirection in which the crack runs, and the second or third principlecomponent is assumed to be the width direction of the crack. Thedirection in which the crack runs may be paraphrased as the direction inwhich the crack spreads, the direction in which the crazing of the crackruns, the direction in which the crazing of the crack spreads, or thelike. For the shape of a crack, since the length in the direction inwhich the crack runs is longest, the first principle component is thedirection in which the crack runs. One of the second principle componentand the third principle component is the width direction of the crack,and the other is the depth direction of the crack.

The crack determination unit 13 may define, for example, the secondprinciple component as the width direction of the crack and the thirdprinciple component as the depth direction of the crack. Alternatively,the crack determination unit 13 may define which of the second principlecomponent and the third principle component corresponds to the widthdirection of the crack by using a learning model machine-learned theshapes of cracks.

Here, the depth direction of the crack is a main component including ameasurement error of the 3D-LiDAR device that is the measurement device.The measurement error is an error that occurs when the distance from themeasurement device to an object is measured. The crack determinationunit 13 may decide which of the second principle component and the thirdprinciple component corresponds to the width direction of the crackbased on the measurement error data or catalog values specified in the3D-LiDAR device. For example, the crack determination unit 13 may decidethat the principle component whose length of the variance is within theprescribed measurement error data specified in the 3D-LiDAR device isthe depth direction of the crack and that the principle component whoselength of the variance exceeds the measurement error data is the widthdirection of the crack.

The crack determination unit 13 may determine whether the set of pointsdetected as the edge indicates a crack based on, for example, the lengthof the principle component corresponding to the width direction of thecrack. For example, suppose that the crack determination unit 13 decidesthat the second principle component corresponds to the width directionof the crack. In this case, the crack determination unit 13 maydetermine whether the distribution of the points detected as the edgeindicates a crack according to whether the length of the secondprinciple component exceeds a prescribed length threshold. For example,if the length of the second principle component exceeds the prescribedlength threshold, the crack determination unit 13 may determine that theset of points detected as the edge is not a crack. On the other hand, ifthe length of the second principle component does not exceed theprescribed length threshold, the crack determination unit 13 maydetermine that the set of points detected as the edge is a crack. Thelength threshold to be compared with the length of the second principlecomponent may be, for example, a value entered by an administrator ofthe detection device 10 or the like, or a value calculated using alearning model machine-learned the shapes of cracks.

Next, a procedure of a crack determination process of the detectiondevice 10 according to the second example embodiment is described withreference to FIG. 4 . First, an acquisition unit 11 acquires point clouddata and luminance information associated with each point in the pointcloud data (S11). The luminance information associated with each pointis the luminance information about reflected light reflected at thatpoint.

Then, the edge detection unit 12 performs edge detection using theluminance information (S12). The edge detection unit 12 detects, as anedge, a point with the difference from the luminance of an adjacentpoint exceeding a prescribed threshold.

Then, the edge detection unit 12 extracts points having luminanceequivalent to the luminance of the point detected as the edge (S13). Inother words, the edge detection unit 12 extracts a plurality of pointshaving luminance whose difference from the luminance of the pointdetected as the edge is within a prescribed range. The plurality ofpoints extracted by the edge detection unit 12 is distributed in athree-dimensional space.

Then, the edge detection unit 12 performs a principle component analysison the distribution of the extracted points (S14). By performing theprinciple component analysis, the edge detection unit 12 calculates afirst principle component to a third principle component that indicatethe variance or dispersion of the distribution of the points detected asthe edge.

Then, the crack determination unit 13 determines whether a set of pointsdetected as the edge is a crack by using a result of the principlecomponent analysis (S15).

Here, the detailed procedure of the crack determination process in stepS15 in FIG. 4 is described with reference to FIG. 5 .

In the crack determination process, the crack determination unit 13first acquires the lengths of the first principle component to thirdprinciple component in the distribution of the points detected as theedge from the edge detection unit 12 (S21). The lengths of the firstprinciple component to the third principle component indicate thelengths of the variance. The length of the first principle component islongest and the length of the third principle component is shortest.

Then, the crack determination unit 13 decides the principle componentthat can be the width direction of the crack among the first principlecomponent to the third principle component (S22). For example, the crackdetermination unit 13 may decide, among the first principle component tothe third principle component, the second principle component having thesecond longest length to be the principle component in the widthdirection of the crack. Alternatively, the crack determination unit 13may decide which of the second principle component and the thirdprinciple component corresponds to the width direction of the crack byusing a learning model machine-learned the shapes of cracks.Alternatively, the crack determination unit 13 may determine which ofthe second principle component and the third principle componentcorresponds to the width direction of the crack based on the measurementerror data or catalog values specified in the 3D-LiDAR device.

Next, the crack determination unit 13 determines whether the length ofthe principle component corresponding to the width direction of thecrack is less than or equal to a prescribed length threshold (S23). Whendetermining that the length of the principle component corresponding tothe width direction of the crack is less than or equal to the prescribedlength threshold, the crack determination unit 13 determines that theset of points detected as the edge is a crack (S24). When determiningthat the length of the principle component corresponding to the widthdirection of the crack is not less than or equal to the prescribedlength threshold, the crack determination unit 13 determines that theset of points detected as the edge is not a crack (S25).

As explained above, in the second example embodiment, the detectiondevice 10 performs edge detection to identify a shape or area to be acrack candidate. In addition, the detection device 10 performs aprinciple component analysis on the points forming the shape to be thecrack candidate or on the points contained in the area to be the crackcandidate. The detection device 10 determines whether the shape or areato be the crack candidate is a crack based on the lengths of the firstprinciple component to the third principle component obtained as theresult of the principle component analysis. In this manner, thedetection device 10 can extract the shape or area to be a crackcandidate by using luminance information even when sufficient brightnesscannot be ensured for a camera or the like to capture images. Inaddition, the detection device 10 can determine whether the crackcandidate is a crack by performing the principle component analysis onthe points constituting the shape to be the crack candidate or thepoints contained in the area to be the crack candidate and using theanalysis result. As a result, the detection device 10 can identify thelocation of a crack even in locations where sufficient brightness cannotbe ensured.

Third Example Embodiment

Next, an overview of a crack determination process according to a thirdexample embodiment is described. For example, a crack can have not onlya straight line shape but also a zigzag shape, as shown in FIG. 6 . Thesolid line in FIG. 6 is a set of points detected as an edge andindicates the shape of a crack. The long dotted line of the thick dottedlines in FIG. 6 is a first principle component having a length D_1. Theshort dotted line of the thick dotted lines in FIG. 6 is a secondprinciple component having a length D_2. Although not shown in FIG. 6for ease of explanation, the vertical principle component in FIG. 6 is athird principle component.

In a crack having a zigzag shape, D_2 can be the second principlecomponent as shown in FIG. 6 . In this case, the length of the secondprinciple component is longer than that when a crack is a straight lineshape. As a result, when a crack determination unit 13 determineswhether the length of the second principle component exceeds aprescribed length threshold similarly to the second example embodiment,the length of the second principle component exceeds the threshold, andthe crack determination unit 13 determines that the set of points arenot a crack although the set of points is actually a crack.

Therefore, in the third example embodiment, when a zigzag shapeindicates a set of points detected as an edge as shown in FIG. 7 , anedge detection unit 12 divides a space where the points detected as theedge exist into a plurality of spaces. In addition, the edge detectionunit 12 performs a principle component analysis on the set of pointscontained in each divided space. Each space surrounded by dotted linesin FIG. 7 shows one of the divided spaces.

For example, the crack determination unit 13 may determine, of the firstprinciple component to the third principle component calculated for eachspace, whether the length of the second principle component is less thana prescribed length threshold and determine whether the set of points ineach space is a crack.

For example, when the set of points has a shape close to a straight lineas in an Area_1 and an Area_2 in FIG. 7 , the crack determination unit13 determines that the set of points is a crack based on the length ofthe second principle component in many cases. On the other hand, whenthe set of points has a zigzag shape as in an Area_3, the crackdetermination unit 13 can determine that the set of points is not acrack because the length of the second principle component is long.Therefore, the crack determination unit 13 may use the results of theprinciple component analysis on the set of points to determine whetherthe entire set of points is a crack based on the number of spacesdetermined to be a crack among the plurality of spaces that has beensubjected to the crack determination. For example, the crackdetermination unit 13 may determine that the entire set of points is acrack when the number of spaces determined to be a crack is greater thanthe number of spaces determined not to be a crack. Alternatively, thecrack determination unit 13 may determine that the entire set of pointsis a crack when the number of spaces determined to be a crack exceeds aprescribed threshold.

As the number of divisions of the space where the points detected as theedge exist increases, the accuracy of the crack determination process ofthe crack determination unit 13 is improved.

As described above, the crack determination unit 13 according to thethird example embodiment divides a space containing a set of pointsdetected as an edge and performs a principle component analysis on theset of points in the divided spaces. The crack determination unit 13performs the principle component analysis for each divided space anddetermines whether the set of points in each divided space is a crack.In addition, the crack determination unit 13 determines whether theentire set of points detected as the edge is a crack by using the resultof the determination for each divided space. In this manner, by usingthe result of the determination for each divided space, the crackdetermination unit 13 can determine whether the entire set of points isa crack even when the set of points has a zigzag shape in which thelength of the second principle component is long, as shown in FIGS. 6and 7 .

Fourth Example Embodiment

Next, a crack determination process according to a fourth exampleembodiment is described. In the fourth example embodiment, a crackdetermination process using point density is performed. The pointdensity is defined based on the number of points detected as an edge ina certain area. The details of the point density are described withreference to FIG. 8 . The solid line in FIG. 8 indicates a set of pointsdetected as an edge. The area surrounded by the dotted line is an areacontaining the set of points detected as the edge. In FIG. 8 , the areacontaining the set of points detected as the edge is shown as arectangle, but the area can be a circle or any other shape.

The point density is the number of points per unit area and indicates,for example, the degree of density of the points in the area surroundedby the dotted line in FIG. 8 .

When the set of points detected as the edge indicates a crack, thepoints are assumed to exist in an area along the crack. On the otherhand, when the set of points detected as the edge does not indicate acrack, the points are assumed to be uniformly distributed within acertain area. Therefore, the crack determination process according tothe fourth example embodiment presupposes that the point density whenthe set of points detected as the edge indicates a crack is sufficientlysmaller than the point density when the set of points does not indicatea crack.

The area containing the set of points detected as the edge indicated bythe dotted line in FIG. 8 may be defined by, for example, executing anobject recognition process to be performed when an image indicating theset of points is analyzed. The object recognition processing is aprocess that identifies an object contained in an image, surrounds theobject with a rectangle or other shape, and highlights the identifiedobject. Object recognition may be paraphrased as body recognition, imagerecognition, or the like

Alternatively, the area containing the set of points detected as theedge indicated by the dotted line in FIG. 8 may be input by a user whovisually recognizes the set of points detected as the edge in the image.The area containing the set of points detected as the edge may be anarea specified by the user.

Next, a procedure of the crack determination process according to thefourth example embodiment is described with reference to FIG. 9 . First,a crack determination unit 13 calculates a point density of pointsdetected as an edge in a predetermined area (S31). Then, the crackdetermination unit 13 determines whether the point density is less thanor equal to a prescribed threshold (S32). When the point density is lessthan or equal to the prescribed threshold, the crack determination unit13 determines that the set of points detected as the edge in thepredetermined area is a crack (S33). When the point density is not lessthan or equal to the prescribed threshold, the crack determination unit13 determines that the set of points detected as the edge in thepredetermined area is not a crack (S34).

As described above, in the crack determination process according to thefourth example embodiment, the crack determination process using thepoint density is performed. Accordingly, it is possible to determinewhether the set of points extracted based on luminance is a crack.

Fifth Example Embodiment

Next, a configuration example of a detection device 20 according to afifth example embodiment is described with reference to FIG. 10 . Thedetection device 20 has a configuration in which a classification unit21, a display unit 22, a label assignment unit 23, an input receptionunit 24, and a data holding unit 25 are added to the detection device 10in FIG. 1 . The constituent elements of the detection device 20, such asthe classification unit 21 and the like, are software or modules to beprocessed by a processor executing a program stored in a memory.Alternatively, the constituent elements constituting the detectiondevice 20 may be hardware, such as circuits or chips.

The classification unit 21 classifies a set of points determined to be acrack by a crack determination unit 13 into one of a plurality of groupsin accordance with predetermined criteria. Alternatively, theclassification unit 21 may classify a set of points whose luminancedetected by an edge detection unit 12 lowers by a prescribed thresholdor more into one of a plurality of groups in accordance withpredetermined criteria. Here, a set of points determined to be a crackby the crack determination unit 13 in the first to fourth exampleembodiments is treated as a crack candidate in the fifth exampleembodiment. For example, the classification unit 21 may classify a crackcandidate into one of the groups in accordance with the distribution ofluminance information associated with each point contained in the crackcandidate. For example, the set of points contained in one crackcandidate may be expressed as a distribution diagram in a plane withluminance on the horizontal axis and the number of points on thevertical axis, and a plurality of crack candidates having a mean valueof luminance within a predetermined range may be classified as the same.The classification unit 21 may define a plurality of groups by definingseveral ranges, such as classification_1 which is a group of the meanvalue of luminance ranging from A1 to A2, classification_2 which is agroup of the mean value of luminance ranging from A3 to A4, and so on.

Instead of a mean value of luminance, the classification unit 21 mayclassify a crack candidate into one of the groups based on variance,standard deviation, median, mode, or the like.

Alternatively, the classification unit 21 may classify a crack candidateinto one of the groups based on the shape of the crack candidate. Forexample, the classification unit 21 may classify a crack candidate intoone of the groups based on a first principle component to a thirdprinciple component obtained from a principle component analysisperformed on the distribution of the points contained in the crackcandidate. Specifically, the classification unit 21 may classify aplurality of crack candidates whose lengths of the first principlecomponents are within a predetermined range as the same classification.The classification unit 21 may use the second principle component or thethird principle component instead of the first principle component.

The classification unit 21 displays at least one of a plurality of crackcandidates contained in one group on the display unit 22. The displayunit 22 may be, for example, a display. The classification unit 21 maydisplay, on the display unit 22, an arbitrarily selected crack candidateamong a plurality of crack candidates contained in one group.Alternatively, the classification unit 21 may display, on the displayunit 22, a crack candidate, among a plurality of crack candidatescontained in one group, near the boundary of the group. Alternatively,the classification unit 21 may display, on the display unit 22, a crackcandidate with high reliability or a crack candidate with lowreliability.

Here, the reliability assigned to a crack candidate is described. Forexample, reliability may be assigned to a crack candidate according tothe incident angle of a beam emitted from a 3D-LiDAR device used as ameasurement device to an object.

FIG. 11 shows that a 3D-LiDAR system 11_A emits a beam against a wall.The white arrow in FIG. 11 indicates the beam emitted from the 3D-LiDARsystem 11_A. The dotted arrows in FIG. 11 indicate the reflected lightwhen the beam is reflected by the wall surface. The incident angleindicates the incident angle of the beam with respect to the wallsurface.

The intensity of the reflected light becomes too strong when the beamemitted from the 3D-LiDAR system 11_A enters an object perpendicularly,and the intensity of the reflected light is not stable when the incidentangle is shallow. If the intensity of the reflected light is too strong,unstable, or the like, accurate luminance cannot be obtained. Therefore,by defining a range of incident angles where the intensity of reflectedlight is too strong and a range of incident angles where the intensityof reflected light is not stable, the reliability of a crack candidategenerated based on the luminance information obtained from the reflectedlight of a beam within the ranges of incident angles may be set low. Thereliability of a crack candidate generated based on the luminanceinformation obtained from the reflected light of a beam within a rangeof incident angles other than the range of incident angles where theintensity of the reflected light is too strong and the range of incidentangles where the intensity of the reflected light unstable may be sethigh.

After visually recognizing the crack candidate displayed on the displayunit 22, the user determines whether the crack candidate is a crack. Theinput reception unit 24 accepts a determination result input from theuser. For example, when the user determines that the crack candidatedisplayed on the display unit 22 is a crack, the user inputs informationindicating that the crack candidate is a crack to the input receptionunit 24. When the user determines that the crack candidate displayed onthe display unit 22 is not a crack, the user inputs informationindicating that the crack candidate is not a crack to the inputreception unit 24. The information indicating that the crack candidateis a crack and the information indicating that the crack candidate isnot a crack may be referred to as, for example, a label. That is, theuser inputs a label indicating whether the crack candidate displayed onthe display unit 22 is a crack or not.

The label assignment unit 23 associates the group containing the crackcandidate displayed on the display unit 22 by the classification unit 21with the label input to the input reception unit 24 and stores them inthe data holding unit 25.

Next, a procedure of a label assignment process according to the fifthexample embodiment is described with reference to FIG. 12 . The processshown in FIG. 12 presupposes that steps S11 to S15 in FIG. 4 have beenperformed and that the crack determination unit 13 has completed thecrack determination process for a set of points detected as an edge.

First, the classification unit 21 classifies a set of points determinedto be a crack into one of a plurality of groups in accordance withprescribed criteria (S41). For example, the classification unit 21 mayclassify the set of points into one of the plurality of groups accordingto the mean value or the like of the luminance of each point in the setof points. If there is more than one set of points determined to be acrack, the classification unit 21 classifies each set of points into oneof the plurality of groups.

Then, the classification unit 21 selects a set of points to berepresentative from sets of points in each group (S42). Theclassification unit 21 may select a set of points to be representativearbitrarily or according to other criteria.

Then, the classification unit 21 displays the selected set of points onthe display unit 22 (S43). In other words, the classification unit 21displays the shape indicated by the selected set of points as a crackcandidate on the display unit 22.

Then, the label assignment unit 23 associates a label for the set ofpoints input by the user with the group containing the set of points andstores them in the data holding unit 25 (S44).

As described above, the detection device 20 classifies a plurality ofcrack candidates determined to be a crack by the crack determinationunit 13 into one of groups. In addition, the detection device 20associates a label input by a user who visually recognizes at least onecrack candidate in the group with the group. That is, the user does notneed to visually recognize all the crack candidates determined to be acrack by the crack determination unit 13 to assign a label to each crackcandidate. The user visually recognizes at least one crack candidate ina group and assign a label to the group containing the crack candidate.That is, the user can collectively assign a label to other crackcandidates in the same group that contains the visually recognized crackcandidate. As a result, the number of crack candidates that the user hasto visually recognize in order to assign a label can be reduced.

By the user visually recognizing a crack candidate extracted by thecrack determination unit 13 and assigning a label indicating whether thecrack candidate is a crack, it is possible to improve accuracy regardingcrack determination.

FIG. 13 is a block diagram showing a configuration example of thedetection device 10 and the detection device 20 (hereinafter, referredto as the detection device 10 or the like). Referring to FIG. 13 , thedetection device 10 or the like includes a network interface 1201, aprocessor 1202, and a memory 1203. The network interface 1201 is used tocommunicate with network nodes (e.g., eNBs, MMEs, or P-GWs). The networkinterface 1201 may include, for example, a network interface card (NIC)in compliance with the IEEE 802.3 series. Here, eNB stands for evolvedNode B, MME stands for Mobility Management Entity, and P-GW stands forPacket Data Network Gateway. IEEE stands for Institute of Electrical andElectronics Engineers.

The processor 1202 loads software (a computer program) from the memory1203 and executes it to perform the processes of the detection device 10or the like described with reference to the flowcharts in the aboveexample embodiments. The processor 1202 may be, for example, amicroprocessor, an MPU, or a CPU. The processor 1202 may include aplurality of processors.

The memory 1203 is configured by a combination of a volatile memory anda nonvolatile memory. The memory 1203 may include a storage located awayfrom the processor 1202. In this case, the processor 1202 may access thememory 1203 via an Input/Output (I/O) interface that is not shown.

In the example in FIG. 13 , the memory 1203 is used to store a group ofsoftware modules. The processor 1202 loads the group of software modulesfrom the memory 1203 and executes them to perform the processes of thedetection device 10 or the like described in the above exampleembodiments.

As described with reference to FIG. 13 , each processor included in thedetection device 10 or the like in the above example embodimentsexecutes one or more programs containing a set of instructions forcausing a computer to execute the algorithm described with reference tothe drawings.

In the above examples, the one or more programs can be stored by varioustypes of non-transitory computer-readable media and provided to acomputer. Non-transitory computer-readable media include any type oftangible storage media. Examples of non-transitory computer-readablemedia include magnetic storage media (such as flexible disks, magnetictapes, and hard disk drives), optical magnetic storage media (such asmagneto-optical disks), Compact Disc Read Only Memory (CD-ROM), CD-R,CD-R/W, and semiconductor memories (such as mask ROM, Programmable ROM(PROM), Erasable PROM (EPROM), flash ROM, and Random Access Memory(RAM)). The one or more programs may be provided to a computer using anytype of transitory computer-readable media. Examples of transitorycomputer-readable media include electric signals, optical signals, andelectromagnetic waves. Transitory computer readable media can providethe one or more programs to a computer through a wired communicationline (such as electric wires, and optical fibers) or a wirelesscommunication line.

Note that, the present disclosure is not limited to the above exampleembodiments and can be modified without departing from the gist thereof.

A part or all of the above example embodiments may be described as thefollowing Supplementary notes but are not limited to the following.

(Supplementary Note 1)

A detection device comprising:

-   -   an acquisition unit configured to acquire point cloud data        indicating a distance from a measurement device to an object and        luminance information obtained from reflected light of a beam        emitted when the point cloud data is measured;    -   an edge detection unit configured to perform edge detection        based on the luminance information; and    -   a crack determination unit configured to determine whether an        area indicates a crack by using a shape of the area in which, of        a plurality of points indicated by the point cloud data, points        having luminance within a predetermined range of difference from        luminance of a point detected as an edge are distributed.

(Supplementary Note 2)

The detection device according to Supplementary note 1, wherein thecrack determination unit is configured to determine whether the areaindicates a crack by using variance of the points identified based onthe distribution of the points having the luminance within thepredetermined range of difference from the luminance of the pointdetected as the edge.

(Supplementary Note 3)

The detection device according to Supplementary note 2, wherein thecrack determination unit is configured to perform a principle componentanalysis on the distribution of the points having the luminance withinthe predetermined range of difference from the luminance of the pointdetected as the edge and to calculate the variance.

(Supplementary Note 4)

The detection device according to Supplementary note 3, wherein thecrack determination unit is configured to perform the principlecomponent analysis on the distribution of the points having theluminance within the predetermined range of difference from theluminance of the point detected as the edge and to determine whether thearea indicates a crack by using a width of the edge defined according toa value of the variance.

(Supplementary Note 5)

The detection device according to Supplementary note 3 or 4, wherein thecrack determination unit is configured to perform the principlecomponent analysis on the distribution of the points having theluminance within the predetermined range of difference from theluminance of the point detected as the edge and to determine whether thearea indicates a crack by using, of a plurality of axes definedaccording to a calculated direction of the distribution of the pointsand the value of the variance, the width of the edge indicated by one ofaxes excluding an axis having a largest value of the variance.

(Supplementary Note 6)

The detection device according to Supplementary note 1, wherein thecrack determination unit is configured to determine whether the areaindicates a crack by using a point density of points contained in theshape of the area in which the points having the luminance within thepredetermined range of difference from the luminance of the pointdetected as the edge are distributed.

(Supplementary Note 7)

The detection device according to Supplementary note 1, wherein the edgedetection unit is configured to generate at least two or more dividedareas by dividing the area in which the points having the luminancewithin the predetermined range of difference from the luminance of thepoint detected as the edge are distributed, and

-   -   the crack determination unit is configured to determine whether        an area in which points contained in each divided area are        distributed indicates a crack by using a shape of a set of        points contained in the divided area.

(Supplementary Note 8)

The detection device according to Supplementary note 7, wherein thecrack determination unit is configured to determine whether the area inwhich the points contained in the divided area are distributed indicatesa crack by using variance of the points identified based on thedistribution of the points in the divided area.

(Supplementary Note 9)

The detection device according to Supplementary note 8, wherein thecrack determination unit is configured to perform a principle componentanalysis on the distribution of the points in the divided area and tocalculate the variance.

(Supplementary Note 10)

The detection device according to Supplementary note 9, wherein thecrack determination unit is configured to perform the principlecomponent analysis on the distribution of the points in the divided areaand to determine whether the area in which the points contained in thedivided area are distributed indicates a crack by using a width of theedge defined according to a value of the variance.

(Supplementary Note 11)

The detection device according to Supplementary note 9 or 10, whereinthe crack determination unit is configured to perform the principlecomponent analysis on the distribution of the points in the divided areaand to determine whether the area in which the points contained in thedivided area are distributed indicates a crack by using, of a pluralityof axes defined according to a calculated direction of the distributionof the points and the value of the variance, the width of the edgeindicated by one of axes excluding an axis having a largest value of thevariance.

(Supplementary Note 12)

A determination method comprising:

-   -   acquiring point cloud data indicating a distance from a        measurement device to an object and luminance information        obtained from reflected light of a beam emitted when the point        cloud data is measured;    -   performing edge detection based on the luminance information;        and    -   determining whether an area indicates a crack by using a shape        of the area in which, of a plurality of points indicated by the        point cloud data, points having luminance within a predetermined        range of difference from luminance of a point detected as an        edge are distributed.

(Supplementary Note 13)

A non-transitory computer-readable medium storing a program causing acomputer to execute:

-   -   acquiring point cloud data indicating a distance from a        measurement device to an object and luminance information        obtained from reflected light of a beam emitted when the point        cloud data is measured;    -   performing edge detection based on the luminance information;        and    -   determining whether an area indicates a crack by using a shape        of the area in which, of a plurality of points indicated by the        point cloud data, points having luminance within a predetermined        range of difference from luminance of a point detected as an        edge are distributed.

REFERENCE SIGNS LIST

-   -   10 Detection device    -   11 Acquisition unit    -   12 Edge detection unit    -   13 Crack determination unit    -   20 Detection device    -   21 Classification unit    -   22 Display unit    -   23 Label assignment unit    -   24 Input reception unit    -   25 Data holding unit

What is claimed is:
 1. A detection device comprising: at least onememory storing instructions, and at least one processor configured toexecute the instructions to; acquire point cloud data indicating adistance from a measurement device to an object and luminanceinformation obtained from reflected light of a beam emitted when thepoint cloud data is measured; perform edge detection based on theluminance information; and determine whether an area indicates a crackby using a shape of the area in which, of a plurality of pointsindicated by the point cloud data, points having luminance within apredetermined range of difference from luminance of a point detected asan edge are distributed.
 2. The detection device according to claim 1,wherein the at least one processor is further configured to execute theinstructions to determine whether the area indicates a crack by usingvariance of the points identified based on the distribution of thepoints having the luminance within the predetermined range of differencefrom the luminance of the point detected as the edge.
 3. The detectiondevice according to claim 2, wherein the at least one processor isfurther configured to execute the instructions to perform a principlecomponent analysis on the distribution of the points having theluminance within the predetermined range of difference from theluminance of the point detected as the edge and calculate the variance.4. The detection device according to claim 3, wherein the at least oneprocessor is further configured to execute the instructions to performthe principle component analysis on the distribution of the pointshaving the luminance within the predetermined range of difference fromthe luminance of the point detected as the edge and determine whetherthe area indicates a crack by using a width of the edge definedaccording to a value of the variance.
 5. The detection device accordingto claim 3, wherein the at least one processor is further configured toexecute the instructions to perform the principle component analysis onthe distribution of the points having the luminance within thepredetermined range of difference from the luminance of the pointdetected as the edge and determine whether the area indicates a crack byusing, of a plurality of axes defined according to a calculateddirection of the distribution of the points and the value of thevariance, the width of the edge indicated by one of axes excluding anaxis having a largest value of the variance.
 6. The detection deviceaccording to claim 1, wherein is configured to the at least oneprocessor is further configured to execute the instructions to determinewhether the area indicates a crack by using a point density of pointscontained in the shape of the area in which the points having theluminance within the predetermined range of difference from theluminance of the point detected as the edge are distributed.
 7. Thedetection device according to claim 1, wherein the at least oneprocessor is further configured to execute the instructions to generateat least two or more divided areas by dividing the area in which thepoints having the luminance within the predetermined range of differencefrom the luminance of the point detected as the edge are distributed,and determine whether an area in which points contained in each dividedarea are distributed indicates a crack by using a shape of a set ofpoints contained in the divided area.
 8. The detection device accordingto claim 7, wherein the at least one processor is further configured toexecute the instructions to determine whether the area in which thepoints contained in the divided area are distributed indicates a crackby using variance of the points identified based on the distribution ofthe points in the divided area.
 9. The detection device according toclaim 8, wherein the at least one processor is further configured toexecute the instructions to perform a principle component analysis onthe distribution of the points in the divided area and calculate thevariance.
 10. The detection device according to claim 9, wherein the atleast one processor is further configured to execute the instructions toperform the principle component analysis on the distribution of thepoints in the divided area and determine whether the area in which thepoints contained in the divided area are distributed indicates a crackby using a width of the edge defined according to a value of thevariance.
 11. The detection device according to claim 9, wherein the atleast one processor is further configured to execute the instructions toperform the principle component analysis on the distribution of thepoints in the divided area and determine whether the area in which thepoints contained in the divided area are distributed indicates a crackby using, of a plurality of axes defined according to a calculateddirection of the distribution of the points and the value of thevariance, the width of the edge indicated by one of axes excluding anaxis having a largest value of the variance.
 12. A determination methodcomprising: acquiring point cloud data indicating a distance from ameasurement device to an object and luminance information obtained fromreflected light of a beam emitted when the point cloud data is measured;performing edge detection based on the luminance information; anddetermining whether an area indicates a crack by using a shape of thearea in which, of a plurality of points indicated by the point clouddata, points having luminance within a predetermined range of differencefrom luminance of a point detected as an edge are distributed.
 13. Anon-transitory computer-readable medium storing a program causing acomputer to execute: acquiring point cloud data indicating a distancefrom a measurement device to an object and luminance informationobtained from reflected light of a beam emitted when the point clouddata is measured; performing edge detection based on the luminanceinformation; and determining whether an area indicates a crack by usinga shape of the area in which, of a plurality of points indicated by thepoint cloud data, points having luminance within a predetermined rangeof difference from luminance of a point detected as an edge aredistributed.